The S-multi-SNE projections are treated as features in the classification algorithm that predicts the classes of the test samples. Different classification algorithms can be utilised for this purpose. Through a series of experiments we illustrate that the K-Nearest Neighbours (KNN)[1]} classification algorithm has a good performance. An advantage of KNN is that a good classification score ensures a good visualisation of the data. That is, because KNN separates different classes into neighbourhoods and classifies the samples in the test set by looking at their neighbours [1]}.
